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Instructions to use Yntec/AnythingRemix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Yntec/AnythingRemix with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Yntec/AnythingRemix", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Note: The model that used to be here has been renamed AnythingRemixAlpha.
Anything Remix
A mix of my favorite anything models, Anything 4.0 and Anything 4.5 to bring the best of both worlds into a single model! Now with AnythingV7's base block to do everything better! It has the MoistMixV2VAE baked in. Work in progress.
Anything Remix Alpha
The original version, check a sample at: https://huggingface.co/Yntec/AnythingRemix/discussions/3
Original pages:
https://huggingface.co/xyn-ai/anything-v4.0
https://huggingface.co/shibal1/anything-v4.5-clone
https://huggingface.co/Yntec/AnythingV7
Recipes
- SuperMerger Weight sum Train Difference Use MBW 1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0
Model A:
anything v4.0
Model B:
anything v4.5
Output Model:
AnythingRemixAlpha
- Bake MoistMixV2VAE in
Output Model:
AnythingRemixAlphaVAE
- SuperMerger Weight sum Use MBW 1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1
Model A:
Anything v7.0
Model B:
AnythingRemix
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